EDS-NLP provides a set of spaCy components that are used to extract information from clinical notes written in French.
If it’s your first time with spaCy, we recommend you familiarise yourself with some of their key concepts by looking at the “spaCy 101” page.
You can install EDS-NLP via
pip install edsnlp
We recommend pinning the library version in your projects, or use a strict package manager like Poetry.
pip install edsnlp==0.4.0
A first pipeline
Once you’ve installed the library, let’s begin with a very simple example that extracts mentions of COVID19 in a text, and detects whether they are negated.
import spacy nlp = spacy.blank("fr") terms = dict( covid=["covid", "coronavirus"], ) # Sentencizer component, needed for negation detection nlp.add_pipe("eds.sentences") # Matcher component nlp.add_pipe("eds.matcher", config=dict(terms=terms)) # Negation detection nlp.add_pipe("eds.negation") # Process your text in one call ! doc = nlp("Le patient est atteint de covid") doc.ents # Out: (covid,) doc.ents._.negation # Out: False
Go to the documentation for more information!
The performances of an extraction pipeline may depend on the population and documents that are considered.
Contributing to EDS-NLP
We welcome contributions ! Fork the project and propose a pull request. Take a look at the dedicated page for detail.